
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns
from warnings import filterwarnings 
filterwarnings("ignore")

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score


data = pd.read_csv("C:\\Users\\laxma\\Downloads\\diabetes.csv")
data


data.head()


data.tail()


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Pregnancies               768 non-null    int64  
 1   Glucose                   768 non-null    int64  
 2   BloodPressure             768 non-null    int64  
 3   SkinThickness             768 non-null    int64  
 4   Insulin                   768 non-null    int64  
 5   BMI                       768 non-null    float64
 6   DiabetesPedigreeFunction  768 non-null    float64
 7   Age                       768 non-null    int64  
 8   Outcome                   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB


data.isnull().sum()

Pregnancies                 0
Glucose                     0
BloodPressure               0
SkinThickness               0
Insulin                     0
BMI                         0
DiabetesPedigreeFunction    0
Age                         0
Outcome                     0
dtype: int64


data.duplicated().sum()

0


data.Insulin.sum()

61286


data.Insulin

0        0
1        0
2        0
3       94
4      168
      ... 
763    180
764      0
765    112
766      0
767      0
Name: Insulin, Length: 768, dtype: int64


data.columns

Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],
      dtype='object')


#VISUALIZATION


plt.bar(data['Outcome'],data['Age'])
plt.xticks(rotation=90)
plt.show()


fig=px.bar(data,x='Pregnancies',y='Outcome',color='Pregnancies')
fig.show()


fig=px.violin(data,x='SkinThickness',y='BMI',color='SkinThickness')
fig.show()


plt.figure(figsize=(10,4))
sns.countplot(x='Outcome', data=data, color='b')
plt.xticks(rotation=90)
plt.show()


plt.figure(figsize=(10,4))
top_car = data['DiabetesPedigreeFunction'].value_counts().nlargest(10)
sns.countplot(y=data.DiabetesPedigreeFunction, order=top_car.index, color='red')

<AxesSubplot:xlabel='count', ylabel='DiabetesPedigreeFunction'>


sns.lineplot(x='BloodPressure', y='Age', data=data).set_title('Variation of Glucose with BMI')

Text(0.5, 1.0, 'Variation of Glucose with BMI')


sns.barplot(data['Outcome'],data['SkinThickness'],color='r')
plt.xticks(rotation=90)
plt.show()


plt.figure(figsize=(8, 4))
sns.scatterplot(data=data, x='BMI', y='Age')
plt.title('BMI and there Age')
plt.xlabel('BMI')
plt.ylabel('Age')
plt.show()


sns.displot(data["Age"])

<seaborn.axisgrid.FacetGrid at 0x24d89f93910>


sns.boxplot(x='BloodPressure',y='Glucose',data=data)
plt.xticks(rotation=90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]),
 [Text(0, 0, '0'),
  Text(1, 0, '24'),
  Text(2, 0, '30'),
  Text(3, 0, '38'),
  Text(4, 0, '40'),
  Text(5, 0, '44'),
  Text(6, 0, '46'),
  Text(7, 0, '48'),
  Text(8, 0, '50'),
  Text(9, 0, '52'),
  Text(10, 0, '54'),
  Text(11, 0, '55'),
  Text(12, 0, '56'),
  Text(13, 0, '58'),
  Text(14, 0, '60'),
  Text(15, 0, '61'),
  Text(16, 0, '62'),
  Text(17, 0, '64'),
  Text(18, 0, '65'),
  Text(19, 0, '66'),
  Text(20, 0, '68'),
  Text(21, 0, '70'),
  Text(22, 0, '72'),
  Text(23, 0, '74'),
  Text(24, 0, '75'),
  Text(25, 0, '76'),
  Text(26, 0, '78'),
  Text(27, 0, '80'),
  Text(28, 0, '82'),
  Text(29, 0, '84'),
  Text(30, 0, '85'),
  Text(31, 0, '86'),
  Text(32, 0, '88'),
  Text(33, 0, '90'),
  Text(34, 0, '92'),
  Text(35, 0, '94'),
  Text(36, 0, '95'),
  Text(37, 0, '96'),
  Text(38, 0, '98'),
  Text(39, 0, '100'),
  Text(40, 0, '102'),
  Text(41, 0, '104'),
  Text(42, 0, '106'),
  Text(43, 0, '108'),
  Text(44, 0, '110'),
  Text(45, 0, '114'),
  Text(46, 0, '122')])


sns.violinplot(x='Pregnancies',y='Age',data=data)

<AxesSubplot:xlabel='Pregnancies', ylabel='Age'>


#MODEL BUILDING


X = data.iloc[:,0:8]
y = data.iloc[:,8]

Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, random_state=4)


sc = StandardScaler()

Xtr = sc.fit_transform(Xtr)
Xte = sc.fit_transform(Xte)


clf=KNeighborsClassifier(n_neighbors=11, p=2, metric='euclidean')


clf.fit(Xtr,ytr)
pred = clf.predict(Xte)


print(confusion_matrix(pred, yte))

[[88 25]
 [14 27]]


print(accuracy_score(pred, yte))

0.7467532467532467

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1
...	...	...	...	...	...	...	...	...	...
763	10	101	76	48	180	32.9	0.171	63	0
764	2	122	70	27	0	36.8	0.340	27	0
765	5	121	72	23	112	26.2	0.245	30	0
766	1	126	60	0	0	30.1	0.349	47	1
767	1	93	70	31	0	30.4	0.315	23	0